def run_grader(part_id):
graded_funcs = {
"r9hUX": Test_tf_constant,
"kWeLo": Test_tf_square,
"E1weD": Test_tf_reshape,
"AuDvh": Test_tf_cast,
"NI9Co": Test_tf_multiply,
"Yz2KA": Test_tf_add,
"4uBzv": Test_tf_gradient_tape
}
g_func = graded_funcs.get(part_id)
if g_func is None:
print_stderr("The partID provided did not correspond to any graded function.")
return
failed_cases, num_cases = g_func()
score = 1.0 - len(failed_cases) / num_cases
if failed_cases:
failed_msg = ""
for failed_case in failed_cases:
failed_msg += f"Failed {failed_case.get('name')}.\nExpected:\n{failed_case.get('expected')},\nbut got:\n{failed_case.get('got')}.\n\n"
send_feedback(score, failed_msg)
else:
send_feedback(score, "All tests passed! Congratulations!")
def run_grader(part_id):
graded_funcs = {
"pxgPU": Test_map_fn,
"fX2cS": Test_set_adam_optimizer,
"M6COK": Test_set_sparse_cat_crossentropy_loss,
"fbLog": Test_set_sparse_cat_crossentropy_accuracy,
"FaW22": Test_prepare_dataset,
"C8oAS": Test_train_one_step,
"NG4Bn": Test_train
}
g_func = graded_funcs.get(part_id)
if g_func is None:
print_stderr(
"The partID provided did not correspond to any graded function.")
return
failed_cases, num_cases = g_func()
score = 1.0 - len(failed_cases) / num_cases
if failed_cases:
failed_msg = ""
for failed_case in failed_cases:
failed_msg += f"Failed {failed_case.get('name')}.\nExpected:\n{failed_case.get('expected')},\nbut got:\n{failed_case.get('got')}.\n\n"
send_feedback(score, failed_msg)
else:
send_feedback(score, "All tests passed! Congratulations!")
def run_grader(part_id):
graded_funcs = {
"xV8vX": Test_distribute_datasets,
"dmTKR": Test_train_test_step_fns,
"cst37": Test_distributed_train_test_step_fns
}
g_func = graded_funcs.get(part_id)
if g_func is None:
print_stderr(
"The partID provided did not correspond to any graded function.")
return
try:
failed_cases, num_cases = g_func()
except:
traceback.print_exc()
send_feedback(
0.0,
"There was a problem grading your submission. Check stderr for more details."
)
exit()
score = 1.0 - len(failed_cases) / num_cases
if failed_cases:
failed_msg = ""
for failed_case in failed_cases:
failed_msg += f"Failed {failed_case.get('name')}.\nExpected:\n{failed_case.get('expected')},\nbut got:\n{failed_case.get('got')}.\n\n"
send_feedback(score, failed_msg)
else:
send_feedback(score, "All tests passed! Congratulations!")
def run_grader(part_id):
graded_funcs = {
"mylyu": Test_F1ScoreResult,
"YQutN": Test_apply_gradients,
"8dNmL": Test_train_data_for_one_epoch
}
g_func = graded_funcs.get(part_id)
if g_func is None:
print_stderr("The partID provided did not correspond to any graded function.")
return
failed_cases, num_cases = g_func()
score = 1.0 - len(failed_cases) / num_cases
if failed_cases:
failed_msg = ""
for failed_case in failed_cases:
failed_msg += f"Failed {failed_case.get('name')}.\nExpected:\n{failed_case.get('expected')},\nbut got:\n{failed_case.get('got')}.\n\n"
send_feedback(score, failed_msg)
else:
send_feedback(score, "All tests passed! Congratulations!")
"mylyu": Test_F1ScoreResult,
"YQutN": Test_apply_gradients,
"8dNmL": Test_train_data_for_one_epoch
}
g_func = graded_funcs.get(part_id)
if g_func is None:
print_stderr("The partID provided did not correspond to any graded function.")
return
failed_cases, num_cases = g_func()
score = 1.0 - len(failed_cases) / num_cases
if failed_cases:
failed_msg = ""
for failed_case in failed_cases:
failed_msg += f"Failed {failed_case.get('name')}.\nExpected:\n{failed_case.get('expected')},\nbut got:\n{failed_case.get('got')}.\n\n"
send_feedback(score, failed_msg)
else:
send_feedback(score, "All tests passed! Congratulations!")
if __name__ == "__main__":
try:
part_id = sys.argv[2]
except IndexError:
print_stderr("Missing partId. Required to continue.")
send_feedback(0.0, "Missing partId.")
else:
run_grader(part_id)
import os
import shutil
import jupytext
from tools import send_feedback
submission_dir = "/shared/submission/"
# submission_dir = "sub2/"
for file in os.listdir(submission_dir):
if file.endswith('.ipynb'):
learner_notebook = file
else:
learner_notebook = None
if learner_notebook is None:
send_feedback(0.0, "No notebook was found in the submission directory.")
exit()
sub_source = submission_dir + learner_notebook
sub_destination = '/grader/submission/submission.ipynb'
# sub_destination = 'submission/submission.ipynb'
shutil.copyfile(sub_source, sub_destination)
nb = jupytext.read("submission/submission.ipynb")
jupytext.write(nb, 'submission/submission.py', fmt='py:percent')
import os
import shutil
from zipfile import ZipFile
from tools import send_feedback
submission_dir = "/shared/submission/"
for file in os.listdir(submission_dir):
if file.endswith('.zip'):
learner_file = file
else:
learner_file = None
if learner_file is None:
send_feedback(0.0, "No .zip was found in the submission directory.")
exit()
sub_source = submission_dir + learner_file
sub_destination = '/grader/mymodel.zip'
shutil.copyfile(sub_source, sub_destination)
saved_model_path = "./mymodel.zip"
with ZipFile(saved_model_path, "r") as zipObj:
zipObj.extractall("./")
from disable_warnings import *
import sys
import tensorflow as tf
import tensorflow_datasets as tfds
from tools import send_feedback, print_stderr
import converter
if __name__ == "__main__":
try:
part_id = sys.argv[2]
except IndexError:
print_stderr("Missing partId. Required to continue.")
send_feedback(0.0, "Missing partId.")
exit()
else:
if part_id != "wNSsr":
print_stderr("Invalid partId. Required to continue.")
send_feedback(0.0, "Invalid partId.")
exit()
try:
student_model = tf.saved_model.load("./tmp/mymodel/1")
except:
send_feedback(
0.0,
"Your model could not be loaded. Make sure the zip file has the correct contents."
)
infer = student_model.signatures["serving_default"]
splits = ["train[:80%]", "train[80%:90%]", "train[90%:]"]